| name | model-router |
| description | How SKULL picks the right model for each agent and task. Triggers on "which model", "pick a model", "model selection", "use haiku/sonnet/opus", "cheaper model", "model per agent", or whenever spawning a subagent/team and choosing its model, or optimizing cost vs. quality on a long run. Gives a role→model heuristic, the "turn count beats token price" rule, the effort dial, and the cost math so the team is both strong where it matters and cheap where it doesn't. |
| allowed-tools | Read, Grep, Glob, Bash, Task |
Model router — the right model for each agent
Multi-agent quality comes mostly from spending tokens across parallel contexts — so spend them where they
matter and save them everywhere else. Set a model per subagent (the model field / the Task model
option): opus · sonnet · haiku · fable · a full id · or inherit (default).
Role → model
| Role / task character | Model | Why |
|---|
| Lead / orchestrator, synthesis, architecture, ambiguous hard reasoning | Opus | Control-logic quality drives the whole run |
| Implementation, refactors, writing tests, most production coding, most reviews | Sonnet | Best capability/cost balance — the production default |
| Search / discovery, grep-and-summarize, mechanical transforms, log triage | Haiku | ~5× cheaper input than Opus; quality gap negligible on non-reasoning work |
| Security-critical or final whole-branch review | Opus | Worth the spend at the gate |
| Sub-task that needs your full context | inherit / fork | Keeps continuity; a fork reuses the prompt cache (cheaper) |
The rule that decides the close calls
Turn count beats token price. A weak model that retries five times costs more — in tokens and
wall-clock — than a stronger fast model that nails it once. When unsure between two tiers for real work,
pick the stronger one; only drop to Haiku for genuinely mechanical tasks.
Two rules that stop silent overspend:
- Least powerful that suffices. Default a role down to the cheapest tier that can actually do it — the
turn-count rule overrides only for genuine reasoning work. (One axis decides up, the other down.)
- Always set the model on a dispatch. An omitted model inherits the session default (often Opus) — so
a grunt task silently runs on the priciest tier, defeating the whole point of routing. Name the model every time.
- Implementation tier by signal: 1–2 files + a complete spec → cheapest; multi-file / integration → mid;
design judgment or broad-codebase reasoning → top. Never drop a reviewer (or an implementer working
from prose, not a precise spec) below the mid tier.
Also dial effort
Independently of the model, set reasoning effort per subagent (low/medium/high/xhigh/max).
Use low for cheap mechanical stages; reserve high+ for the hardest verify/judge/synthesis stages.
Cheap model and low effort for the grunt work; strong model and high effort for the crux.
Cost anchors (per MTok, input/output)
Opus 4.8 $5 / $25 · Sonnet 4.6 $3 / $15 · Haiku 4.5 $1 / $5. So Haiku input is ~5× cheaper than
Opus, Sonnet ~1.7× cheaper. A common, strong shape: Opus lead + Sonnet workers + Haiku scouts.
Defaults & cautions
- Don't override the model without a reason —
inherit is right more often than not.
- The built-in Explore subagent already runs on a fast model and is read-only — prefer it for
"where is X / how does Y work" instead of spinning up a custom researcher.
- Match
effort and model together; a max-effort Haiku on a reasoning task is a false economy.
Credits / further reading: Anthropic pricing
& subagents docs; the "model-per-role / turn-count-beats-price"
framing is crisp in superpowers (obra/superpowers, MIT). See docs/ECOSYSTEM.md. Pair with
subagent-orchestration.
